Prompt Engineering
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Prompt engineering is the process of designing and refining the prompts used to generate text from language models, such as GPT-3 or similar models. The goal of prompt engineering is to improve the quality and relevance of the generated text by carefully crafting the prompts to elicit the desired responses from the model.
Prompt engineering involves several steps, including selecting the appropriate model architecture and parameters, designing the prompt format and structure, selecting the appropriate task and training data, and fine-tuning the model using the selected prompt and data.
Prompt engineering is a crucial step in the development of language models, as it can greatly influence the quality and effectiveness of the model's responses. By carefully designing and refining the prompts used to generate text, researchers and developers can improve the accuracy and relevance of the model's output, making it more useful for a wide range of applications, including chatbots, language translation, content creation, and more.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/taited/clip-score
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/tencent-ailab/ip-adapter
IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models
https://github.com/tencent/hunyuandit
DialogGen: Multi-modal Interactive Dialogue System for Multi-turn Text-to-Image Generation
https://github.com/thu-coai/jailbreakdefense_goalpriority
Jailbreaking ChatGPT via Prompt Engineering: An Empirical Study
https://github.com/thudm/swissarmytransformer
https://github.com/towhee-io/towhee
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/tsinghuac3i/ultramedical
Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine
https://github.com/typefly/TypeFly
https://github.com/ukplab/arxiv2022-context-injection-stance
Multitask Prompted Training Enables Zero-Shot Task Generalization
https://github.com/umass-foundation-model/3d-llm
https://github.com/ux-decoder/segment-everything-everywhere-all-at-once
https://github.com/ux-decoder/semantic-sam
https://github.com/vineet2104/hifics
https://github.com/vitae-transformer/samrs
https://github.com/wgcban/apt
https://github.com/xinghaochen/tinysam
https://github.com/xz-liu/shield
Jailbreaking ChatGPT via Prompt Engineering: An Empirical Study
https://github.com/yangyucheng000/University/tree/main/model-2/gpt_bigcode
https://github.com/ybzh/openood-vlm
LAPT: Label-driven Automated Prompt Tuning for OOD Detection with Vision-Language Models
https://github.com/yiqi0318/deepstpa
Safety Analysis in the Era of Large Language Models: A Case Study of STPA using ChatGPT
https://github.com/ylqi/count-anything
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/yongchao98/promst
https://github.com/yu-yang-li/starglm
Deep Learning and LLM-based Methods Applied to Stellar Lightcurve Classification
https://github.com/yuuun/clip_pytorch
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/yzfly/langgpt
https://github.com/zhangxu0963/npc
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/zhaohan-xi/plm-prompt-defense
Differentiable Prompt Makes Pre-trained Language Models Better Few-shot Learners
https://github.com/zhengxiangshi/powerfulpromptft
Differentiable Prompt Makes Pre-trained Language Models Better Few-shot Learners
https://huggingface.co/julianzy/CheckGPT
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.